13 research outputs found
Minimal Reachability is Hard To Approximate
In this note, we consider the problem of choosing which nodes of a linear
dynamical system should be actuated so that the state transfer from the
system's initial condition to a given final state is possible. Assuming a
standard complexity hypothesis, we show that this problem cannot be efficiently
solved or approximated in polynomial, or even quasi-polynomial, time
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity
The current exponential rise in recording capacity calls for new approaches
for analysing and interpreting neural data. Effective dimensionality has
emerged as a key concept for describing neural activity at the collective
level, yet different studies rely on a variety of definitions of it. Here we
focus on the complementary notions of intrinsic and embedding dimensionality,
and argue that they provide a useful framework for extracting computational
principles from data. Reviewing recent works, we propose that the intrinsic
dimensionality reflects information about the latent variables encoded in
collective activity, while embedding dimensionality reveals the manner in which
this information is processed. Network models form an ideal substrate for
testing more specifically the hypotheses on the computational principles
reflected through intrinsic and embedding dimensionality
Low-dimensional controllability of brain networks
Network controllability is a powerful tool to study causal relationships in
complex systems and identify the driver nodes for steering the network dynamics
into desired states. However, due to ill-posed conditions, results become
unreliable when the number of drivers becomes too small compared to the network
size. This is a very common situation, particularly in real-world applications,
where the possibility to access multiple nodes at the same time is limited by
technological constraints, such as in the human brain. Although targeting
smaller network parts might improve accuracy, challenges may remain for
extremely unbalanced situations, when for example there is one single driver.
To address this problem, we developed a mathematical framework that combines
concepts from spectral graph theory and modern network science. Instead of
controlling the original network dynamics, we aimed to control its
low-dimensional embedding into the topological space derived from the network
Laplacian. By performing extensive simulations on synthetic networks, we showed
that a relatively low number of projected components is enough to improve the
overall control accuracy, notably when dealing with very few drivers. Based on
these findings, we introduced alternative low-dimensional controllability
metrics and used them to identify the main driver areas of the human connectome
obtained from N=6134 healthy individuals in the UK-biobank cohort. Results
revealed previously unappreciated influential regions compared to standard
approaches, enabled to draw control maps between distinct specialized
large-scale brain systems, and yielded an anatomically-based understanding of
hemispheric functional lateralization. Taken together, our results offered a
theoretically-grounded solution to deal with network controllability in
real-life applications and provided insights into the causal interactions of
the human brain
Neurofeedback: principles, appraisal and outstanding issues
Neurofeedback is a form of brain training in which subjects are fed back
information about some measure of their brain activity which they are
instructed to modify in a way thought to be functionally advantageous. Over the
last twenty years, NF has been used to treat various neurological and
psychiatric conditions, and to improve cognitive function in various contexts.
However, despite its growing popularity, each of the main steps in NF comes
with its own set of often covert assumptions. Here we critically examine some
conceptual and methodological issues associated with the way general objectives
and neural targets of NF are defined, and review the neural mechanisms through
which NF may act, and the way its efficacy is gauged. The NF process is
characterised in terms of functional dynamics, and possible ways in which it
may be controlled are discussed. Finally, it is proposed that improving NF will
require better understanding of various fundamental aspects of brain dynamics
and a more precise definition of functional brain activity and brain-behaviour
relationships.Comment: 12 page
Drug-resistant focal epilepsy in children is associated with increased modal controllability of the whole brain and epileptogenic regions
Network control theory provides a framework by which neurophysiological dynamics of the brain can be modelled as a function of the structural connectome constructed from diffusion MRI. Average controllability describes the ability of a region to drive the brain to easy-to-reach neurophysiological states whilst modal controllability describes the ability of a region to drive the brain to difficult-to-reach states. In this study, we identify increases in mean average and modal controllability in children with drug-resistant epilepsy compared to healthy controls. Using simulations, we purport that these changes may be a result of increased thalamocortical connectivity. At the node level, we demonstrate decreased modal controllability in the thalamus and posterior cingulate regions. In those undergoing resective surgery, we also demonstrate increased modal controllability of the resected parcels, a finding specific to patients who were rendered seizure free following surgery. Changes in controllability are a manifestation of brain network dysfunction in epilepsy and may be a useful construct to understand the pathophysiology of this archetypical network disease. Understanding the mechanisms underlying these controllability changes may also facilitate the design of network-focussed interventions that seek to normalise network structure and function
Controllability in complex brain networks
Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Recently it has been proposed to characterize brain networks in terms of their "controllability", drawing on concepts and methods of control theory. The thesis will review the control theory for networks and its application in neuroscience. In particular, the study will highlight important limitations and some warning and caveats in the brain controllability framework.ope
Interictal Network Dynamics in Paediatric Epilepsy Surgery
Epilepsy is an archetypal brain network disorder. Despite two decades of research
elucidating network mechanisms of disease and correlating these with outcomes, the clinical
management of children with epilepsy does not readily integrate network concepts. For
example, network measures are not used in presurgical evaluation to guide decision making
or surgical management plans.
The aim of this thesis was to investigate novel network frameworks from the perspective of
a clinician, with the explicit aim of finding measures that may be clinically useful and
translatable to directly benefit patient care. We examined networks at three different scales,
namely macro (whole brain diffusion MRI), meso (subnetworks from SEEG recordings) and
micro (single unit networks) scales, consistently finding network abnormalities in children
being evaluated for or undergoing epilepsy surgery. This work also provides a path to clinical
translation, using frameworks such as IDEAL to robustly assess the impact of these new
technologies on management and outcomes.
The thesis sets up a platform from which promising computational technology, that utilises
brain network analyses, can be readily translated to benefit patient care
Reduced emergent character of neural dynamics in patients with a disrupted connectome
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as “Integrated Information Decomposition,” which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems — including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients’ structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence